imputeCellMCD: Cellwise MCD-based imputation for mixed data

In VIM: Visualization and Imputation of Missing Values

Cellwise MCD-based imputation for mixed data

Description

Extends the cellwise MCD approach (Raymaekers & Rousseeuw 2024) to mixed continuous + categorical data. Uses MCD for robust covariance estimation of the continuous block, computes cellwise weights from conditional residuals, then imputes missing values via conditional expectations (continuous) and weighted multinomial regression (categorical). Iterates until convergence.

Usage

imputeCellMCD(
  data,
  maxit = 50,
  eps = 0.005,
  method = "tukey",
  alpha = NULL,
  mcd_alpha = 0.75,
  hard_threshold = 0.5,
  mcd_observed = "all",
  init_method = "median",
  uncert = "conditional",
  m = 1L,
  boot = FALSE,
  trace = FALSE
)

Arguments

data	a data.frame with missing values (mixed continuous and categorical variables are supported).
maxit	maximum number of iterations (default: 50).
eps	convergence tolerance (default: 5e-3).
method	weight function for cell weights: "tukey" (default) or "huber".
alpha	tuning constant. NULL (default) uses 4.685 for Tukey and 1.345 for Huber.
mcd_alpha	MCD concentration parameter (default: 0.75).
hard_threshold	numeric in [0, 1]. Before iteration, observed cells with initial MCD weight below this threshold are set to missing and re-imputed (detect-once preprocessing). Set to NULL or 0 to disable (default: 0.5).
mcd_observed	strategy for covariance estimation: "all" (default) runs MCD on all data including imputed values; "weighted" uses cellWise::cwLocScat with imputed cells receiving weight 0 (requires the cellWise package); "pairwise" uses pairwise robust (Gnanadesikan–Kettenring) covariances on observed cells only.
init_method	initialisation for missing values before iteration: "median" (default), "knn", or "irmi".
uncert	imputation uncertainty: "conditional" (default) adds noise from the conditional normal distribution.
m	number of multiple imputations (default: 1). If m > 1, a list of imputed datasets is returned.
boot	logical; intended to add bootstrap parameter uncertainty across the m imputations. Currently ineffective: the imputation step is a per-variable robust regression (cellIRWLS), so resampling the location/covariance estimate leaves the imputations essentially unchanged (measured between-imputation variance is not inflated relative to boot = FALSE). Proper parameter-uncertainty propagation requires bootstrapping the per-variable regression coefficients and is not yet implemented; see the note below. Default FALSE.
trace	logical; if TRUE, print progress information.

Value

A list with components:

data_imputed	the imputed data.frame.
cellweights	n \times p matrix of cell weights. Continuous observed cells have values in [0, 1]; categorical columns always have weight 1 (cellwise detection is only applied to continuous variables).
mu	robust location estimate (continuous variables).
Sigma	robust covariance estimate (continuous variables).
converged	logical indicating convergence.
iterations	number of iterations performed.

Note

Multiple imputation with proper parameter-uncertainty propagation is not yet implemented for this method (see boot). m > 1 returns repeated imputations whose between-imputation variability comes only from the stochastic uncertainty step (uncert) and estimator randomness, so downstream Rubin pooling understates total variance. Treat the current m > 1 output as approximate.

Author(s)

Matthias Templ

References

Raymaekers, J. and Rousseeuw, P.J. (2024). The cellwise minimum covariance determinant estimator. Journal of the American Statistical Association, 119(548), 2610–2621.

See Also

imputeCellIRMI, imputeCellM, imputeCellEM

Other imputation methods: hotdeck(), impPCA(), imputeCellEM(), imputeCellIRMI(), imputeCellM(), imputeCellwise(), imputeRobust(), imputeRobustChain(), irmi(), kNN(), matchImpute(), medianSamp(), rangerImpute(), regressionImp(), sampleCat(), vimmi, vimpute(), xgboostImpute()

Examples

## Not run: 
data(sleep, package = "VIM")
result <- imputeCellMCD(sleep)
head(result$data_imputed)

# Inspect cell weights
image(result$cellweights, main = "Cell weights")

# With pairwise robust covariance
result2 <- imputeCellMCD(sleep, mcd_observed = "pairwise", trace = TRUE)

## End(Not run)

VIM documentation built on Sept. 2, 2026, 5:07 p.m.